Unified Detection of LLM Backdoors via Shared Latent Features
The paper shows that many large language model backdoor attacks activate a common set of latent features detectable with sparse autoencoders. By identifying and suppressing these features, the authors achieve zero‑shot detection across models and propose a training‑time mitigation method, offering a general defense beyond trigger‑specific approaches.